"License",
            "Machine Learning",
            "Mapping",
            "Network",
            "Packaging",
            "Percolator",
            "Performance",
            "Query Languages",
            "Ranking",
            "Recovery",
            "Reindex",
            "Rollup",
            "SQL",
            "Search",
            "Security",
            "Snapshot/Restore",

      matrix:
        # Override automatic language detection by changing the below list
        # Supported options are ['csharp', 'cpp', 'go', 'java', 'javascript', 'python']
        language: ['java', 'javascript']
        # Learn more...
        # https://docs.github.com/en/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#overriding-automatic-language-detection

    steps:

namespace tensorflow {
namespace checkpoint {

class TensorSliceReader;

// A wrapper around BundleReader (for V2 checkpoints) and
// checkpoint::TensorSliceReader (for V1), that is more easily SWIG wrapped for
// other languages.
//
// The class currently only interacts with single-slice (i.e., non-partitioned)
// variables.
class CheckpointReader {
 public:
  CheckpointReader(const std::string& filename, TF_Status* status);

* **Harder for non-native speakers**: Contributors and users for whom English is a second language benefit from a single, consistent convention

While both American and British English are valid, establishing a single standard improves consistency, readability, and maintainability of the codebase.

## Decision

  /**
   * Puts each 16-bit code unit from the {@link CharSequence} into this sink.
   *
   * <p><b>Warning:</b> This method will produce different output than most other languages do when
   * running on the equivalent input. For cross-language compatibility, use {@link #putString},
   * usually with a charset of UTF-8. For other use cases, use {@code putUnencodedChars}.
   *
   * @since 15.0 (since 11.0 as putString(CharSequence))

        final List<String> tags = SuggestUtil.getAsList(source.get(FieldNames.TAGS));
        final List<String> roles = SuggestUtil.getAsList(source.get(FieldNames.ROLES));
        final List<String> languages = SuggestUtil.getAsList(source.get(FieldNames.LANGUAGES));
        final List<String> kinds = SuggestUtil.getAsList(source.get(FieldNames.KINDS));
        final long timestamp = Long.parseLong(source.get(FieldNames.TIMESTAMP).toString());

            final Object languages = context.request.getAttribute(Constants.REQUEST_LANGUAGES);
            if (languages != null) {
                searchLog.setLanguages(StringUtils.join((String[]) languages, ","));
            } else {
                searchLog.setLanguages(StringUtil.EMPTY);
            }

@lru_cache
def get_mkdocs_material_langs() -> list[str]:
    material_path = Path(material.__file__).parent
    material_langs_path = material_path / "templates" / "partials" / "languages"
    langs = [file.stem for file in material_langs_path.glob("*.html")]
    return langs


class EnFile(File):
    pass


def on_config(config: MkDocsConfig, **kwargs: Any) -> MkDocsConfig:

     * <ul>
     * <li>IDs must match, or an IllegalArgumentException is thrown</li>
     * <li>Text is taken from item1</li>
     * <li>Readings are merged, maintaining uniqueness</li>
     * <li>Fields, tags, languages, and roles are merged, maintaining uniqueness</li>
     * <li>Kinds are merged, maintaining uniqueness</li>
     * <li>Frequencies (queryFreq, docFreq) are summed</li>

        String[] fields = { "field1" };
        String[] tags = { "tag1" };
        String[] roles = { "role1" };
        String[] languages = { "en" };

        SuggestItem item = new SuggestItem(text, readings, fields, 100L, 50L, 2.0f, tags, roles, languages, SuggestItem.Kind.QUERY);

        String json = item.toJsonString();

        assertTrue("Should contain text", json.contains("test"));